The image-to-GPS verification problem asks whether a given image is taken at a claimed GPS location. In this paper, we treat it as an image verification problem -- whether a query image is taken at the same place as a reference image retrieved at the claimed GPS location. We make three major contributions: 1) we propose a novel custom bottom-up pattern matching (BUPM) deep neural network solution; 2) we demonstrate that the verification can be directly done by cross-checking a perspective-looking query image and a panorama reference image, and 3) we collect and clean a dataset of 30K pairs query and reference. Our experimental results show that the proposed BUPM solution outperforms the state-of-the-art solutions in terms of both verification and localization.
@article{arxiv.1811.07288,
title = {Image-to-GPS Verification Through A Bottom-Up Pattern Matching Network},
author = {Jiaxin Cheng and Yue Wu and Wael Abd-Almageed and Prem Natarajan},
journal= {arXiv preprint arXiv:1811.07288},
year = {2018}
}